Performance of an iterated T-HMM for homology detection
Bioinformatics
Protein homology detection using string alignment kernels
Bioinformatics
Protein homology detection by HMM--HMM comparison
Bioinformatics
Brief communication: SVM-BALSA: Remote homology detection based on Bayesian sequence alignment
Computational Biology and Chemistry
Computers in Biology and Medicine
ICMLA '11 Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops - Volume 02
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A new method for remote protein homology detection, called support vector machine incorporating the context of physicochemical properties (SVM-CP), is presented. Recent discriminative methods are based on concatenating information extracted from each protein by considering several physicochemical properties. We show that there are physicochemical properties that reflect the functional or structural characteristics of each specific protein family, but there are also some physicochemical properties that affect the accuracy of the classification techniques. The research highlights the importance of the selection of physicochemical properties in remote homology detection. Most of the methods slide a window over every protein sequence to extract physicochemical information. This extraction is usually performed by giving the same importance to every value in the window, i.e., averaging the physicochemical values in the observation window. SVM-CP takes into account that every residue in a sliding window has a different weight, which reflects the importance or contribution to the representative value of the window. The SVM-CP method reaches a receiver operating characteristic (ROC) score of 0.93462, which is the highest value for a remote homology detection method based on the sequence composition information.